A Self-Adaptive Resource Provisioning Approach using Fuzzy Logic for Cloud-Based Applications

Abstract

The resource adaptation on demand is an important factor in the field of cloud computing. During runtime, autonomic resource provisioning is not an easy task to choose the accurate amount of resources for service-based cloud applications. For this reason, it is required to guess the future demands for self-adaptive resources to deal with the irregular requests based on runtime workload changes of service-based cloud applications. In this paper, an efficient approach to increase the utilization of resources is proposed that is based on self-adaptive computing with fuzzy logic. Additionally, the proposed fuzzy logic approach enhanced the performance of planning phase for better decision making. Based on fuzzy logic, cloud applications having self-learning provisioning resources outperformed the hybrid resource provisioning approach. To calculate the quality of the proposed technique, real-world ClarkNet and NASA workload traces are used. The results of experiments show that the proposed technique has decreased the entire cost and has boosted the utilization of resources as compared to the other contemporary techniques.

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

“…This Fig. 8: Fuzzy logic based SuS-RM method involves three main steps: fuzzification, rule evaluation, and defuzzification [246], [247]. During fuzzification, relevant features for decision-making are determined using fuzzy logic [248], [249].…”

Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo

“…A fuzzy logic model is suggested for selecting cloud providers, while another measures the uncertainty in survey responses. Finally, a trust model is introduced to aid users in identifying reliable cloud service providers (Mateen et al, 2020;Alharbi and Alhalabi, 2020;Gireesha et al, 2020;Khorsand et al, 2019;Nagarajan and Thirunavukarasu, 2019;Rizvi et al, 2020;Zhou et al, 2019). A comprehensive summary of literature focusing on CV selection using MCDM and fuzzy methodologies is provided in Table 1.…”